Marcellus Pelcher

"Pull all siloed AI projects into a one-page chart. Focus on the ones aligned with strategic goals, pause the rest, assign owners, remove roadblocks, and make clear what business value each delivers." – Julian Tang, AI Champion, BlackRock In this Pure Storage–sponsored episode of the #AIinBusinessPodcast, Julian Tang joins Emerj Artificial Intelligence Research Editorial Director Matthew DeMello to break down how enterprises can operationalize AI, align projects with strategy, and accelerate measurable outcomes. Retail and CPG leaders can uncover actionable frameworks to integrate AI into workflows, prioritize initiatives, and unlock ROI with greater clarity and speed. Listen to the full episode to discover actionable strategies for aligning AI projects with business goals and accelerating real results: https://lnkd.in/dxxHiDGU #EnterpriseAI #AIGovernance #AIWorkflow

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In this post, we demonstrate how to integrate Amazon SageMaker HyperPod with Anyscale platform to address critical infrastructure challenges in building and deploying large-scale AI models. The combined solution provides robust infrastructure for...

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In this Help Net Security interview, Daniel Stenberg, lead developer od cURL, discusses how the widely used tool remains secure across billions of devices, from cloud services to IoT. He shares insights into cURL’s decades-long journey of testing, reviewing, and refining its code to minimize risks. 🔗 Read more: https://lnkd.in/dyBea3-b #vulnerabilitymanagement #cURL #cybersecurity

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𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗲𝘀𝘀𝗼𝗻 𝗰𝗼𝘀𝘁 𝗼𝘂𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 $𝟲𝟬𝟬 𝗮𝗻𝗱 𝗮 𝗺𝗮𝗿𝗿𝗶𝗮𝗴𝗲 𝗮𝗿𝗴𝘂𝗺𝗲𝗻𝘁. Our VP of Engineering, Xiaofan(James) Luan, was supposed to buy his wife a Dior bag for their anniversary.  Instead, he bought three Claude Code subscriptions and spent the holiday trying to cross-compile 2 million lines of C++. Every fix on one platform broke two others.  $600 later, the only output was "git reset --hard" — and a very cold dinner table.😂 "Make it compile on Windows" is a trap. The real goal was "compile everywhere without hacks" — no AI is going to figure that out for you at 2 am. What worked: constraints before code, review tests not code, bottom-up, one layer at a time. Same task, two days. Then he ran six parallel Claude sessions across three machines with git worktree. The bottleneck stopped being intelligence and started being how fast one person can alt-tab. AI solves exactly the problem you give it. Engineering is in knowing which one to give. His wife is still waiting for that bag. Full story: https://lnkd.in/gtsW_Wvk ——— Follow Milvus, created by Zilliz, for everything related to unstructured data

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New from O’Reilly + Redis: Managing Memory for AI Agents 📘 AI agents are only as good as their memory. This new report explores how short-term, long-term, and persistent memory shape agent performance—and how Redis provides real-time, scalable architectures for production-ready AI. 📖 Read the full report: https://lnkd.in/g7t--57p

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“In a very binary sense, Metronome made self-service at Anyscale possible.” That’s Matt Weber, Software Engineer, reflecting on Anyscale's expansion into new revenue channels. As the team added PLG and cloud marketplaces (AWS, GCP, and Azure), their manual billing setup couldn’t support the volume or complexity. Reporting, reconciliation, and manual workflows were slowing them down. With Metronome as their billing foundation, Anyscale unified every revenue motion on one platform and accelerated growth across the business. They launched self-serve billing end-to-end, brought AWS, GCP, and Azure marketplaces live in under three weeks each, and freed up three engineers and a PM from dedicated billing work. 🎉🤝 Big congratulations to the Anyscale team on the expansion—this is what it looks like when innovative product and modern monetization infrastructure come together. Read the full story at the link in the comments.

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As AI grows more specialized, infrastructure must keep up. F5 and NVIDIA are helping enterprises build scalable, secure AI factories with BIG-IP Next for Kubernetes on BlueField-3 DPUs—enabling faster inference, smarter prompt routing, and better performance for LLMs. Explore how we're supporting the shift to purpose-built AI: https://go.f5.net/q3cy5o44 #F5 #GTCParis #NVIDIA #Kubernetes #LLM #AgenticAI

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